Self-evolution droop multi-scale power adaptive adjustment system and method for emergency power supply

Abstract

The invention discloses an emergency power supply self-evolution droop multi-scale power adaptive adjustment system and method, and relates to the technical field of intelligent emergency power supply control. The system comprises a frequency power droop characteristic modeling subsystem, a frequency sensing power instruction generation subsystem and an adaptive adjustment execution and optimization subsystem. A nonlinear droop relation model between active power and system frequency is established, the system frequency is acquired, an active power instruction is dynamically generated, and an inverter is driven to realize power regulation. According to the invention, autonomous cooperative control of the emergency power supply in an island or micro-grid mode is realized, multi-machine parallel power balance and circulation suppression are supported, multi-time scale response, dynamic parameter self-tuning and closed-loop performance self-optimization capabilities are provided, and system stability, reliability and power supply toughness are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent emergency power supply control technology, and more specifically to an emergency power supply self-evolving drooping multi-scale power adaptive adjustment system and method. Background Technology

[0002] With increasing societal demands for reliable power supply, and the frequent occurrence of various emergencies, natural disasters, and power grid failures, emergency power systems play a crucial role in ensuring continuous power supply to critical loads. This is especially true in areas highly sensitive to power continuity, such as data centers, hospitals, transportation hubs, and important industrial production. The rapid response capability and stable power supply performance of emergency power supplies have become core indicators for measuring their reliability. At the same time, modern power grids are developing towards intelligence, distribution, and multi-source collaboration, which not only places a basic demand on emergency power supplies to "provide power," but also requires them to have intelligent control capabilities that are plug-and-play, seamlessly switch, and autonomously adapt to grid conditions, in order to achieve efficient collaborative operation with the main grid or microgrid systems.

[0003] Existing emergency power supply systems, operating in islanded or microgrid modes, typically employ constant power or master-slave control strategies. They lack the ability to autonomously adjust output power based on real-time grid conditions. This leads to significant fluctuations in system frequency and uneven distribution of active power among emergency power supply units when multiple units operate in parallel or when the load changes dynamically. It can even trigger circulating current, oscillations, or overload protection actions, severely impacting power supply reliability and system stability. The core issue lies in the lack of an autonomous power regulation mechanism based on grid frequency feedback. This makes it impossible to achieve real-time, smooth, and collaborative adaptive response of emergency power supply output power, and it is difficult to meet the high requirements for power quality and dynamic balance under complex operating conditions.

[0004] Therefore, how to ensure that emergency power supplies can achieve real-time, smooth, and coordinated adaptive power regulation under conditions of sudden load changes or parallel operation is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide an emergency power supply self-evolving drooping multi-scale power adaptive adjustment system and method that overcomes or at least partially solves the above problems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an emergency power supply self-evolving droop multi-scale adaptive power regulation system, comprising a frequency power droop characteristic modeling subsystem, a frequency-sensing power command generation subsystem, and an adaptive regulation execution and optimization subsystem:

[0007] The frequency power droop characteristic modeling subsystem is used to establish a nonlinear droop adjustment relationship model between the active power output of the emergency power supply and the real-time frequency of the emergency power supply grid-connected system. The frequency-sensing power command generation subsystem is used to collect the real-time frequency of the emergency power grid-connected system and dynamically calculate the active power command value output by the emergency power supply based on a nonlinear droop adjustment relationship model. The adaptive adjustment execution and optimization subsystem is used to receive the active power command value, drive the inverter control system to complete the dynamic adjustment of active power, obtain the adjustment performance feedback result, and feed the adjustment performance feedback result back to the frequency power droop characteristic modeling subsystem to form a closed-loop control.

[0008] Preferably, the frequency power droop characteristic modeling subsystem includes a self-evolving droop characteristic modeling module, which is used to realize the autonomous coordinated adjustment and dynamic power matching of emergency power supply under complex operating conditions. The self-evolving droop characteristic modeling module includes a frequency response unit, a dynamic characteristic optimization unit, and a multi-source compatible adaptation unit. The frequency response unit is used to construct a nonlinear droop adjustment relationship model between the active power output of the emergency power supply and the real-time frequency deviation of the emergency power supply grid-connected system, and to obtain the reference nonlinear droop function. The dynamic characteristic optimization unit dynamically corrects the local slope of the droop curve of the reference nonlinear droop function based on the frequency change trend, load status, and power supply capacity information of the nonlinear droop adjustment relationship model. The multi-source compatible adapter unit calculates and generates a virtual frequency modulation weighting factor based on the rated capacity, state of charge, and thermal stress level of the emergency power supply, which is used to differentiate the weighting of different emergency power supplies in power allocation.

[0009] Preferably, the frequency-aware power command generation subsystem includes a frequency measurement module and a control strategy generation module; The frequency measurement module collects the frequency of the emergency power grid connection system in real time, and provides the emergency power grid connection system frequency signal to the dynamic droop coefficient self-tuning unit and the inertial response simulation unit. The control strategy generation module generates active power command values ​​based on the frequency change trend, load status, and power supply capacity information of the nonlinear droop adjustment relationship model. The control strategy generation module includes a dynamic droop coefficient self-tuning unit and an inertial response simulation unit; The dynamic droop coefficient self-tuning unit adjusts the overall slope of the droop curve of the reference nonlinear droop function based on the load status and power capacity information of the nonlinear droop adjustment relationship model, combined with the frequency signal of the emergency power grid connection system. The inertial response simulation unit simulates the virtual rotational inertia characteristics of a synchronous generator using the frequency signal of the emergency power grid connection system, thereby suppressing sudden changes in the real-time frequency of the emergency power grid connection system.

[0010] Preferably, the adaptive adjustment execution and optimization subsystem includes an intelligent resilience execution module; The intelligent resilience execution module is used to achieve safety verification of emergency power supply regulation, dynamic response across multiple time scales, and autonomous optimization of control performance. The intelligent resilience execution module includes an emergency power supply unit, a multi-time scale collaborative execution unit, a dynamic power allocation verification unit, and a closed-loop performance self-optimization adjustment unit. An emergency power supply unit is a physical emergency power supply body that provides electrical energy output; The multi-timescale collaborative execution unit is used to divide the active power command into three control levels: millisecond-level instantaneous response, second-level steady-state tracking, and minute-level energy balance. Among them, the millisecond-level instantaneous response is controlled based on the inertial response simulation unit, the second-level steady-state tracking is controlled based on the dynamic droop coefficient self-tuning unit, and the minute-level energy balance is controlled based on the multi-source compatible adaptation unit. The dynamic power allocation verification unit is used to determine whether the active power command value is within the safe response range. If it exceeds the safe response range, it will limit the amplitude and send a collaborative compensation request to the multi-timescale collaborative execution unit. The closed-loop performance self-optimization adjustment unit is used to monitor the deviation between the actual output power of the inverter control system and the active power command value generated by the control strategy generation module. Based on the deviation and frequency change trend, it adaptively adjusts the PI parameters and determines whether the active power command is within the safe response range according to the dynamic power allocation verification unit. For active power within the safe response range, it drives the inverter control system to complete dynamic adjustment and obtain the adjustment performance feedback result.

[0011] Preferably, the formula for calculating the required active power command value for dynamic calculation is as follows:

[0012] in, This indicates the active power command value. This represents the reference value for rated active power. Indicates the reference droop gain. This represents the nonlinear gain correction coefficient. This represents the virtual frequency modulation weighting factor composed of multi-source compatible adapter units. Indicates the measured system frequency. Indicates the rated frequency. Indicates the non-downward index. Represents the virtual moment of inertia coefficient. It represents the rate of change of frequency.

[0013] An emergency power supply self-evolving droop multi-scale adaptive power adjustment method specifically includes the following steps: S1. Establish a nonlinear droop adjustment relationship model between the active power output of the emergency power supply and the real-time frequency of the emergency power supply grid-connected system; S2. Collect the real-time frequency of the emergency power grid-connected system and dynamically calculate the active power command value output by the emergency power supply based on the nonlinear droop adjustment relationship model. S3 receives the active power command value, drives the inverter control system to complete the dynamic adjustment of active power, obtains the adjustment performance feedback result, and feeds the adjustment performance feedback result back to the frequency power droop characteristic modeling subsystem to form a closed-loop control.

[0014] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses an emergency power supply self-evolving droop multi-scale power adaptive adjustment system and method. The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least the following: 1. By constructing an adaptive adjustment and control system based on the active frequency droop characteristic, autonomous and coordinated control of emergency power supply in islanded or microgrid operation modes was realized, significantly improving the stability and reliability of the power supply system.

[0015] 2. The system integrates frequency response, dynamic optimization and multi-source adaptation capabilities through a self-evolving droop characteristic modeling module. Combined with inertial response simulation and dynamic droop coefficient self-tuning mechanism, each emergency power supply can autonomously adjust its output power according to real-time frequency changes without the need for master station communication. This effectively suppresses frequency mutations and achieves balanced power distribution and circulating current suppression under multi-unit parallel operation.

[0016] 3. The intelligent resilience execution module further introduces safety verification, multi-timescale collaborative response, and closed-loop performance self-optimization mechanism to ensure that the power regulation process takes into account instantaneous response, steady-state tracking, and long-term energy sustainability. At the same time, it improves the system's adaptability to different power supply characteristics, load disturbances, and operating conditions. The overall solution realizes plug-and-play, seamless switching, and intelligent operation of emergency power supplies, significantly improving power quality, system robustness, and power supply resilience under complex operating conditions. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1This is a schematic diagram of the emergency power supply self-evolving drooping multi-scale power adaptive adjustment system and method provided in the embodiments of the present invention; Figure 2 This is a flowchart of the emergency power supply self-evolving drooping multi-scale power adaptive adjustment system and method provided in the embodiments of the present invention; Figure 3 This is a schematic diagram of the structure of a storage medium provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a computing device provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] This invention discloses an emergency power supply self-evolving droop multi-scale adaptive power regulation system, including a frequency power droop characteristic modeling subsystem, a frequency-sensing power command generation subsystem, and an adaptive regulation execution and optimization subsystem. The frequency power droop characteristic modeling subsystem is used to establish a nonlinear droop adjustment relationship model between the active power output of the emergency power supply and the real-time frequency of the emergency power supply grid-connected system. The frequency-sensing power command generation subsystem is used to collect the real-time frequency of the emergency power grid-connected system and dynamically calculate the active power command value output by the emergency power supply based on a nonlinear droop adjustment relationship model. The adaptive adjustment execution and optimization subsystem is used to receive the active power command value, drive the inverter control system to complete the dynamic adjustment of active power, obtain the adjustment performance feedback result, and feed the adjustment performance feedback result back to the frequency power droop characteristic modeling subsystem to form a closed-loop control.

[0021] This invention provides an emergency power adaptive regulation system based on self-evolving droop characteristics and multi-time-scale collaborative control through a specific embodiment, comprising: S1: Frequency power droop characteristic modeling subsystem, used to establish a nonlinear droop adjustment relationship model between the active power output of the emergency power supply and the real-time frequency of the system.

[0022] The frequency power droop characteristic modeling subsystem includes a self-evolving droop characteristic modeling module, which is used to complete the autonomous coordinated adjustment and dynamic power matching of emergency power supplies under complex operating conditions without the need for master station communication.

[0023] Furthermore, the frequency power droop characteristic modeling subsystem enables the emergency power supply to possess the autonomous frequency response characteristics of a synchronous generator by presetting a dynamic droop coefficient and a frequency reference value, which is used to trigger power regulation commands when the system frequency deviates from the rated value; the frequency sensing power command generation subsystem acquires frequency signals through the frequency measurement module.

[0024] The adaptive adjustment execution and optimization subsystem includes comparing the deviation between the active power command value and the actual output power of the inverter control system through a closed-loop feedback mechanism, correcting the control parameters, completing the autonomous power equalization allocation of the emergency power unit, and completing the adaptive dynamic adjustment of the emergency power.

[0025] The frequency-power droop characteristic modeling subsystem, the frequency-sensing power command generation subsystem, and the adaptive adjustment execution and optimization subsystem constitute an organically coordinated closed-loop control system. Among them, the frequency-power droop characteristic modeling subsystem is responsible for establishing a nonlinear dynamic mapping relationship between the active power output of the emergency power supply and the system frequency. This model integrates multi-dimensional operating information such as frequency deviation, frequency change rate, power supply capacity, state of charge, and thermal stress to form a droop adjustment rule with adaptive capabilities.

[0026] Based on this, the frequency-sensing power command generation subsystem not only collects the real-time frequency signal of the emergency power grid system, but more importantly, according to the above-mentioned nonlinear droop adjustment relationship model, combined with the current grid frequency status and power supply operating conditions, it actively calculates and generates specific active power command values. Therefore, its function goes far beyond signal acquisition, but truly completes the entire process of sensing, decision-making, and command generation.

[0027] Subsequently, the command is sent to the adaptive adjustment execution and optimization subsystem, where the closed-loop performance self-optimization adjustment unit performs dynamic correction. This unit continuously compares the deviation between the actual active power output of the inverter control system and the generated active power command value. Based on the magnitude and trend of the deviation and the system frequency fluctuation characteristics, it automatically adjusts the proportional-integral parameters and nonlinear gain strategy of the underlying control loop and introduces an anti-saturation mechanism to prevent the control quantity from exceeding the limit. At the same time, in the scenario of multiple machines in parallel, each emergency power supply achieves autonomous power equalization allocation without communication through this closed-loop mechanism based on local frequency information and its own adjustment capability, thereby completing the adaptive power regulation control of the entire system.

[0028] The self-evolving droop characteristic modeling module includes a frequency response unit, a dynamic characteristic optimization unit, and a multi-source compatible adaptation unit.

[0029] The frequency response unit is used to construct a reference nonlinear droop function between the active power output of the emergency power supply and the system frequency deviation.

[0030] The dynamic characteristic optimization unit is used to correct the local slope of the droop curve of the reference nonlinear droop function based on the rate of change of system frequency and the intensity of current load mutation.

[0031] The multi-source compatible adapter unit is used to identify the rated capacity, state of charge, and thermal stress level of the emergency power supply and generate a virtual frequency modulation weighting factor.

[0032] The self-evolutionary mechanism does not refer to the autonomous reconstruction of the model structure or evolution in the sense of machine learning, but rather to the ability of the droop characteristic to dynamically and autonomously adjust its response behavior and control parameters based on real-time operating conditions and the power supply's own state during operation, thereby continuously optimizing power distribution performance.

[0033] Furthermore, this mechanism is achieved collaboratively by three sub-units within the self-evolving droop characteristic modeling module: the frequency response unit first establishes a benchmark nonlinear droop function between the active power of the emergency power supply and the system frequency deviation, serving as the benchmark framework for regulation; based on this, the dynamic characteristic optimization unit corrects the local slope of the droop curve in real time according to the rate of change of the system frequency and the intensity of load mutations; for example, it increases the regulation sensitivity when the frequency drops rapidly and reduces the response gain when under light load steady state, in order to balance stability and dynamism; the multi-source compatible adaptation unit obtains operating status information such as rated capacity, state of charge, and thermal stress from the power supply itself, and generates a virtual frequency regulation weighting factor accordingly, which is used to differentiate the power allocation for emergency power supplies of different states or types, ensuring that units with high power and low thermal load undertake more regulation tasks. The combined action of these three units transforms the droop characteristic from a fixed static curve into a dynamic regulation mechanism that can self-adjust and self-adapt with the intensity of grid disturbances and the health status of the power supply, thus embodying the core concept of self-evolution; that is, realizing the online evolution and collaborative optimization of the droop control strategy without external intervention or communication conditions.

[0034] The virtual frequency regulation weighting factor is a normalized regulation coefficient generated by the multi-source compatible adapter unit based on a comprehensive evaluation of three key operating parameters of the emergency power supply: rated capacity, state of charge (SOC), and thermal stress level. It reflects the power supply's current availability or priority in participating in system frequency regulation. Its definition logic is as follows: the larger the rated capacity, the stronger the basic regulation capability; the higher the SOC, the greater the margin of sustainable output power; the lower the thermal stress, the higher the equipment safety margin, and the more suitable it is for regulation tasks. The specific calculation rule adopts a weighted fusion method with itemized scoring: first, each parameter is normalized and mapped according to a preset threshold range (e.g., a weight of 1.0 when the SOC is between 50% and 80%, linearly decreasing below 50%, and moderately increasing above 80%); the weight is significantly reduced when the thermal stress exceeds a safety threshold (e.g., 85℃); then, the three normalized values ​​are weighted and summed according to a set ratio (e.g., capacity 40%, SOC 40%, thermal stress 20%), and after limiting and smoothing, a virtual frequency regulation weighting factor between 0.6 and 1.0 is finally generated. This factor directly affects the calculation process of the droop power command, enabling high-capacity, high-health emergency power supplies to automatically share more regulation power, thereby achieving fair, safe, and efficient collaboration among multi-source heterogeneous power supplies.

[0035] Furthermore, the local slope range is dynamically adjusted according to the system frequency deviation and the intensity of load changes. Its value range is 0.8%Hz / kW to 1.2%Hz / kW under light frequency load, 0.5%Hz / kW to 0.9%Hz / kW under moderate frequency deviation, and can be instantly expanded to 0.3%Hz / kW to 0.6%Hz / kW when the frequency drops rapidly or a large load is applied, in order to enhance the adjustment sensitivity. At the same time, combined with the response strategy of the dynamic characteristic optimization unit, this range is switched within milliseconds to ensure that the droop characteristics are always in the optimal adjustment range under different operating conditions.

[0036] The self-evolution of this invention does not refer to the system's adaptive optimization to complex disturbances under masterless communication conditions. Specifically, it manifests in three aspects: First, the slope of the droop curve can be automatically corrected by the dynamic characteristic optimization unit based on the frequency change rate (df / dt) and the intensity of load mutations, improving adjustment sensitivity; second, the virtual frequency modulation weighting factor is generated in real time by the multi-source compatible adaptation unit based on the power supply's rated capacity, state of charge, and thermal stress level, enabling emergency power supplies in different states to rationally share power according to their own capabilities; third, the closed-loop performance self-optimizing adjustment unit automatically optimizes the nonlinear gain and anti-saturation strategy of the PI control parameters by monitoring power tracking deviation and frequency fluctuation patterns, and feeds this back to the nonlinear droop adjustment relationship model, forming continuous performance iteration. These mechanisms together constitute the self-evolution capability; that is, online adaptive evolution and collaborative optimization of droop characteristics can be achieved without external intervention during operation.

[0037] S2: Frequency-sensing power command generation subsystem, used to collect the system frequency of the emergency power grid-connected system and dynamically calculate the required output active power command value based on a nonlinear droop adjustment relationship model.

[0038] The frequency-sensing power command generation subsystem includes a frequency measurement module and a control strategy generation module. The frequency measurement module is used to acquire the real-time frequency value of the system and provide raw input data for the entire control system. The control strategy generation module is used to dynamically adjust the control parameters based on the frequency change trend, load status and power capacity information.

[0039] Furthermore, the frequency measurement module includes a dynamic droop coefficient self-tuning unit and an inertial response simulation unit.

[0040] The dynamic droop coefficient self-tuning unit is used to adjust the overall slope of the droop curve according to the load change rate and power capacity of the emergency power supply, so that emergency power supplies of different capacities or operating states can share power according to their own capabilities.

[0041] The inertial response simulation unit is used to simulate the virtual rotational inertia characteristics of a synchronous generator and suppress sudden changes in the real-time frequency of the emergency power grid connection system.

[0042] The frequency measurement module provides input signals for the dynamic droop coefficient self-tuning unit and the inertial response simulation unit.

[0043] Furthermore, the formula for calculating the active power command value required for dynamic calculation is as follows:

[0044] in, This indicates the active power command value. This represents the reference value for rated active power. Indicates the reference droop gain. This represents the nonlinear gain correction coefficient. This represents the virtual frequency modulation weighting factor composed of multi-source compatible adapter units. Indicates the measured system frequency. Indicates the rated frequency. Indicates the non-downward index. Represents the virtual moment of inertia coefficient. It represents the rate of change of frequency.

[0045] S3: Adaptive adjustment execution and optimization subsystem, used to receive active power command values ​​and drive the inverter control system to complete the dynamic adjustment of active power.

[0046] The adaptive adjustment execution and optimization subsystem includes an intelligent resilience execution module, which is used to realize the safety verification of power regulation of emergency power supply under variable load and complex power grid environment, dynamic response at multiple time scales and autonomous optimization of control performance.

[0047] Furthermore, the intelligent resilience execution module includes an emergency power supply unit, a dynamic power distribution verification unit, a multi-timescale collaborative execution unit, and a closed-loop performance self-optimization adjustment unit.

[0048] An emergency power supply unit is a physical emergency power source that provides electrical energy output.

[0049] The dynamic power allocation verification unit is used to determine whether the active power command value is within the safe response range after receiving the active power command. If it exceeds the safe response range, it limits the value according to the upper limit of the capacity and sends a coordinated compensation request to the nearby emergency power source to ensure the physical feasibility of the adjustment command and the system power balance.

[0050] Furthermore, if the active power command value exceeds the dynamically determined safety response range, which is clearly quantified in the specific embodiments of this application: the lower limit of the safety response range is 10% of the rated power of the emergency power supply, and the upper limit is usually 90% of the rated power; when the state of charge is higher than 80%, the upper limit can be temporarily increased to 95% (lasting no more than 30 seconds); when the state of charge is between 50% and 80%, the upper limit remains at 90%; when the state of charge is lower than 50%, the upper limit starts from 80% and decreases by 5 percentage points for every 5% decrease in state of charge, but not lower than 60%; in addition, if the thermal stress exceeds 85°C, the upper limit will automatically decrease by another 10 percentage points. When the generated power command exceeds the above dynamic boundaries, the system will limit the command to the current capacity limit and send a collaborative compensation request to the nearby emergency power supply to ensure that the single-machine command is physically achievable, while maintaining system-level power balance through multi-machine cooperation.

[0051] The multi-timescale collaborative execution unit is used to divide the power regulation command process into three control levels: millisecond-level instantaneous response, second-level steady-state tracking, and minute-level energy balance.

[0052] Among them, the millisecond-level instantaneous response is controlled by an inertial response simulation unit, the second-level steady-state tracking is controlled by a dynamic droop coefficient self-tuning unit, and the minute-level energy balancing is controlled by a multi-source compatible adaptation unit. The inverter control system output is driven by a hierarchical weighted fusion strategy, so that the emergency power supply can remain sustainable in response to both short-term impacts and long-term load changes.

[0053] The closed-loop performance self-optimization adjustment unit is used to monitor the deviation between the actual output power of the inverter control system and the active power command value generated by the control strategy generation module. Based on the deviation and frequency change trend, it adaptively adjusts the nonlinear gain curve of the PI parameter and the anti-saturation strategy.

[0054] PI parameters refer to the ability to track active power commands by adjusting the dynamic response speed error elimination capability of the voltage or current control loop.

[0055] Preferably, the safety response range is dynamically determined based on the rated power, state of charge, and thermal stress level of the emergency power supply unit, with a lower limit of 10% of the rated power and an upper limit of 90% of the rated power; when the state of charge is higher than 80%, the upper limit can be temporarily increased to 95%, with a duration not exceeding 30 seconds; The safety response range is calculated in real time by the dynamic power distribution verification unit in the intelligent resilience execution module. This unit continuously acquires three key state parameters of the emergency power supply: rated power (an inherent parameter of the device, stored in the local configuration), state of charge (provided in real time by the battery management system BMS), and thermal stress level (collected by a temperature sensor and converted into a thermal load index). Based on the clearly given rules (lower limit of 10%, upper limit of 90%, and dynamic correction logic of state of charge and temperature to the upper limit), it makes online judgments and limits.

[0056] When the state of charge is between 50% and 80%, the upper limit is 90%; when the state of charge is below 50%, the upper limit is lowered to 80%, and decreases by 5 percentage points for every 5% decrease in state of charge, with a minimum of no less than 60%; when the thermal stress exceeds 85℃, the upper limit is automatically reduced by 10 percentage points to ensure the safe operation of the equipment.

[0057] Furthermore, the emergency power adaptive adjustment and control system includes a frequency measurement module that collects the frequency of the emergency power grid-connected system in real time during system operation and inputs it to the self-evolving droop characteristic modeling module. The frequency response unit inside the self-evolving droop characteristic modeling module establishes a benchmark nonlinear droop relationship. The dynamic characteristic optimization unit dynamically corrects the droop parameter based on the frequency change rate and load mutation. The multi-source compatible adaptation unit combines power supply capacity, state of charge and thermal stress to generate virtual frequency modulation weights, forming an adaptive adjustment model. The adaptive adjustment model, in collaboration with the dynamic droop coefficient self-tuning unit and the inertial response simulation unit, generates an active power command with dynamic response and inertial support capabilities. After the active power command is verified for safety by the dynamic power distribution verification unit in the intelligent resilience execution module, it is decomposed into three control levels by the multi-timescale collaborative execution unit: millisecond-level instantaneous response, second-level steady-state tracking, and minute-level energy balance. The active power command within the safe response range drives the inverter control system to complete dynamic adjustment and obtain the adjustment performance feedback results. At the same time, the closed-loop performance self-optimizing adjustment unit monitors the deviation and optimizes the PI parameters, feeding back the performance feedback results to the self-evolving droop characteristic modeling module to complete the adaptive adjustment of emergency power supply.

[0058] The comparison between the present invention and the prior art is shown in Table 1 below: Table 1. Comparison of the advantages of this invention and existing technical solutions

[0059] Table 1 compares the core differences between the prior art and the present invention in emergency power control, highlighting the comprehensive advantages of the present invention in terms of no communication required, adaptive adjustment, multi-machine collaboration, dynamic response, and safety and stability, demonstrating its technological advancement and practicality.

[0060] In one specific embodiment, an adaptive adjustment and control method for emergency power supply with active power frequency droop characteristics is provided. The method includes establishing a nonlinear droop adjustment relationship model between the output active power of the emergency power supply and the real-time frequency of the system; acquiring the system frequency of the grid-connected emergency power supply system; dynamically calculating the required output active power command value based on the nonlinear droop adjustment relationship model; receiving the active power command value; and driving the inverter control system to complete the dynamic adjustment of active power.

[0061] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0062] In one specific embodiment, the present invention provides a computer device, which can be a terminal. The computer device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0063] In summary, this invention, by constructing an adaptive adjustment and control system based on active power frequency droop characteristics, achieves autonomous and coordinated control of emergency power supplies in islanded or microgrid operation modes, significantly improving the stability and reliability of the power supply system. The system integrates frequency response, dynamic optimization, and multi-source adaptation capabilities through a self-evolving droop characteristic modeling module, combined with inertial response simulation and dynamic droop coefficient self-tuning mechanism. This allows each emergency power supply to autonomously adjust its output power according to real-time frequency changes without master station communication, effectively suppressing frequency mutations and achieving balanced power distribution and circulating current suppression under multi-machine parallel operation. The intelligent resilience execution module further introduces safety verification, multi-timescale coordinated response, and closed-loop performance self-optimization mechanism to ensure that the power adjustment process takes into account instantaneous response, steady-state tracking, and long-term energy sustainability. At the same time, it improves the system's adaptability to different power supply characteristics, load disturbances, and operating conditions. The overall solution achieves plug-and-play, seamless switching, and intelligent operation of emergency power supplies, significantly improving power quality, system robustness, and power supply resilience under complex operating conditions.

[0064] After introducing the methods and systems of exemplary embodiments of the present invention, refer to Figure 3 The computer-readable storage medium of the exemplary embodiments of the present invention will be described, and the specific implementation of each step will not be repeated here.

[0065] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0066] After introducing the methods and media of exemplary embodiments of the present invention, the following references are made. Figure 4 A computational device for adaptive recovery of low-voltage power grid self-healing control according to an exemplary embodiment of the present invention.

[0067] Figure 4 A block diagram of an exemplary computing device 40 for implementing embodiments of the present invention is shown. The computing device 40 may be a computer system or a server.

[0068] like Figure 4 As shown, the components of computing device 40 may include, but are not limited to: one or more processors or processing units 401, system memory 402, and bus 403 connecting different system components (including system memory 402 and processing unit 401).

[0069] The computing device 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 40, including volatile and non-volatile media, and removable and non-removable media.

[0070] System memory 402 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 4021 and / or cache memory 4022. Computing device 40 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 4023 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 4 (Not shown in the image, usually referred to as "hard drive"). Although not shown in... Figure 4 The diagram illustrates that disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) can be provided. In these cases, each drive can be connected to bus 403 via one or more data media interfaces. System memory 402 may include at least one program product having a set of program modules configured to perform the functions of the embodiments of the present invention.

[0071] A program / utility 4025 having a set of program modules 4024 may be stored, for example, in system memory 402, and such program modules 4024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment. Program modules 4024 typically perform the functions and / or methods described in the embodiments of the present invention.

[0072] The computing device 40 can also communicate with one or more external devices 404 (such as a keyboard, pointing device, display, etc.). This communication can be performed via the input / output (I / O) interface 405. Furthermore, the computing device 40 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 406. Figure 4 As shown, network adapter 406 communicates with other modules of computing device 40 (such as processing unit 401) via bus 403. It should be understood that, although... Figure 4 As not shown, it can be used in conjunction with computing device 40 with other hardware and / or software modules.

[0073] The processing unit 401 executes various functional applications and data processing by running programs stored in the system memory 402. For example, the frequency-power droop characteristic modeling subsystem is used to establish a nonlinear droop adjustment relationship model between the emergency power supply's output active power and the system's real-time frequency; the frequency-sensing power command generation subsystem is used to collect the system frequency of the emergency power supply's grid-connected system and dynamically calculate the required output active power command value based on the nonlinear droop adjustment relationship model; the adaptive adjustment execution and optimization subsystem is used to receive the active power command value and drive the inverter control system to complete the dynamic adjustment of active power; the frequency-power droop characteristic modeling subsystem includes a self-evolving droop characteristic modeling module, which is used to complete the autonomous coordinated adjustment and dynamic power matching of the emergency power supply under complex operating conditions without master station communication; the frequency-sensing power command generation subsystem includes a frequency measurement module and a control strategy generation module; the adaptive adjustment execution and optimization subsystem includes an intelligent resilience execution module, which is used to realize the safety verification, multi-timescale dynamic response, and autonomous optimization of control performance of the emergency power supply's power adjustment under variable loads and complex grid environments.

[0074] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0075] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0076] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0078] If the functionality is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0079] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0080] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An emergency power supply self-evolving droop multi-scale adaptive power adjustment system, characterized in that, It includes a frequency power droop characteristic modeling subsystem, a frequency-aware power command generation subsystem, and an adaptive adjustment execution and optimization subsystem. The frequency power droop characteristic modeling subsystem is used to establish a nonlinear droop adjustment relationship model between the active power output of the emergency power supply and the real-time frequency of the emergency power supply grid-connected system. The frequency-sensing power command generation subsystem is used to collect the real-time frequency of the emergency power grid-connected system and dynamically calculate the active power command value output by the emergency power supply based on the nonlinear droop adjustment relationship model. The adaptive adjustment execution and optimization subsystem is used to receive the active power command value, drive the inverter control system to complete the dynamic adjustment of active power, obtain the adjustment performance feedback result, and feed the adjustment performance feedback result back to the frequency power droop characteristic modeling subsystem to form a closed-loop control.

2. The emergency power supply self-evolving droop multi-scale power adaptive adjustment system according to claim 1, characterized in that, The frequency power droop characteristic modeling subsystem includes a self-evolving droop characteristic modeling module, which is used to realize the autonomous coordinated adjustment and dynamic power matching of emergency power supplies under complex operating conditions. The self-evolving droop characteristic modeling module includes a frequency response unit, a dynamic characteristic optimization unit, and a multi-source compatible adaptation unit. The frequency response unit is used to construct a nonlinear droop adjustment relationship model between the active power output of the emergency power supply and the real-time frequency deviation of the emergency power supply grid-connected system, and to obtain the reference nonlinear droop function. The dynamic characteristic optimization unit dynamically corrects the local slope of the droop curve of the reference nonlinear droop function based on the frequency change trend, load status, and power capacity information of the nonlinear droop adjustment relationship model. The multi-source compatible adapter unit calculates and generates a virtual frequency modulation weighting factor based on the rated capacity, state of charge, and thermal stress level of the emergency power supply, which is used to differentiate the weighting of different emergency power supplies in power allocation.

3. The emergency power supply self-evolving droop multi-scale power adaptive adjustment system according to claim 2, characterized in that, The frequency-sensing power command generation subsystem includes a frequency measurement module and a control strategy generation module; The frequency measurement module collects the frequency of the emergency power grid-connected system in real time, and provides the emergency power grid-connected system frequency signal to the dynamic droop coefficient self-tuning unit and the inertial response simulation unit. The control strategy generation module generates active power command values ​​based on the frequency change trend, load status, and power supply capacity information of the nonlinear droop adjustment relationship model. The control strategy generation module includes a dynamic droop coefficient self-tuning unit and an inertial response simulation unit; The dynamic droop coefficient self-tuning unit adjusts the overall slope of the droop curve of the reference nonlinear droop function based on the load status and power capacity information of the nonlinear droop adjustment relationship model, combined with the frequency signal of the emergency power grid connection system. The inertial response simulation unit simulates the virtual rotational inertia characteristics of a synchronous generator using the frequency signal of the emergency power grid connection system, thereby suppressing sudden changes in the real-time frequency of the emergency power grid connection system.

4. The emergency power supply self-evolving droop multi-scale adaptive power adjustment system according to claim 3, characterized in that, The adaptive adjustment execution and optimization subsystem includes an intelligent resilience execution module; The intelligent resilience execution module is used to realize the safety verification of emergency power regulation, multi-timescale dynamic response, and autonomous optimization of control performance. The intelligent resilience execution module includes an emergency power unit, a multi-timescale collaborative execution unit, a dynamic power allocation verification unit, and a closed-loop performance self-optimization adjustment unit. The emergency power supply unit is a physical emergency power supply body that provides electrical energy output; The multi-timescale collaborative execution unit is used to divide the active power command into three control levels: millisecond-level instantaneous response, second-level steady-state tracking, and minute-level energy balance. The millisecond-level instantaneous response is controlled based on the inertial response simulation unit, the second-level steady-state tracking is controlled based on the dynamic droop coefficient self-tuning unit, and the minute-level energy balance is controlled based on the multi-source compatible adaptation unit. The dynamic power allocation verification unit is used to determine whether the active power command value is within the safe response range. If it exceeds the safe response range, it will limit the amplitude and send a collaborative compensation request to the multi-time scale collaborative execution unit. The closed-loop performance self-optimization adjustment unit is used to monitor the deviation between the actual output power of the inverter control system and the active power command value generated by the control strategy generation module. Based on the deviation and the frequency change trend, it adaptively adjusts the PI parameters and determines whether the active power command is within the safe response range according to the dynamic power allocation verification unit. For active power commands within the safe response range, it drives the inverter control system to complete dynamic adjustment and obtains the adjustment performance feedback result.

5. The emergency power supply self-evolving droop multi-scale power adaptive adjustment system according to claim 1, characterized in that, The formula for calculating the active power command value required for the dynamic calculation is as follows: in, This indicates the active power command value. This represents the reference value for rated active power. Indicates the reference droop gain. This represents the nonlinear gain correction coefficient. This represents the virtual frequency modulation weighting factor composed of multi-source compatible adapter units. Indicates the measured system frequency. Indicates the rated frequency. This indicates the non-downward index. Represents the virtual moment of inertia coefficient. It represents the rate of change of frequency.

6. A self-evolving droop multi-scale adaptive power adjustment method for emergency power supplies, implemented according to any one of the self-evolving droop multi-scale adaptive power adjustment systems for emergency power supplies as described in claims 1-7, characterized in that... Includes the following steps: S1. Establish a nonlinear droop adjustment relationship model between the active power output of the emergency power supply and the real-time frequency of the emergency power supply grid-connected system; S2. Collect the real-time frequency of the emergency power grid connection system, and dynamically calculate the active power command value output by the emergency power supply based on the nonlinear droop adjustment relationship model. S3. Receive the active power command value, drive the inverter control system to complete the dynamic adjustment of active power, obtain the adjustment performance feedback result, and feed the adjustment performance feedback result back to the frequency power droop characteristic modeling subsystem to form a closed-loop control.

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